AmberYifan/capsd-opc-dedup-marin-8b-base-code_ppl_b4000_s0
The AmberYifan/capsd-opc-dedup-marin-8b-base-code_ppl_b4000_s0 model is an 8 billion parameter language model, fine-tuned from marin-community/marin-8b-base. It was trained on a specialized dataset, capsd_marin-8b-base-n80000-opc-dedup80k__mix_code_ppl_b4000_s0, suggesting an optimization for code-related tasks. This model is designed for applications requiring a compact yet capable base model with potential enhancements for specific programming language understanding or generation.
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Model Overview
This model, AmberYifan/capsd-opc-dedup-marin-8b-base-code_ppl_b4000_s0, is an 8 billion parameter language model. It is a fine-tuned variant of the marin-community/marin-8b-base architecture, indicating a foundation in a robust base model. The fine-tuning process utilized a specific dataset named capsd_marin-8b-base-n80000-opc-dedup80k__mix_code_ppl_b4000_s0, which implies a focus on code-related data, potentially for improved performance in programming contexts.
Training Details
The model was trained with a learning rate of 1e-05, a batch size of 2 (with 8 gradient accumulation steps, totaling an effective batch size of 64), and an AdamW optimizer. A cosine learning rate scheduler was employed with 0.03 warmup steps over a single epoch. The training leveraged a multi-GPU setup with 4 devices. The development environment included Transformers 5.7.0, Pytorch 2.13.0+cu130, Datasets 4.0.0, and Tokenizers 0.22.2.
Potential Use Cases
Given its fine-tuning on a code-centric dataset, this model is likely suitable for:
- Code completion and generation tasks.
- Code summarization or explanation.
- Assisting with programming-related queries.
Further evaluation is needed to confirm specific performance metrics and optimal applications.